1. Field of the Invention
The invention relates to recommendation services and, more specifically, to methods for providing more focused item recommendations to users.
2. Description of the Related Art
A recommendation service is a computer-implemented service that recommends items stored or represented in a data repository. The recommendations are customized to particular users based on information known about the users. One common application for recommendation services involves recommending products for purchase, rental, subscription, or some other form of consumption. For example, online merchants commonly provide services for recommending books, compact discs, videos, and other types of products to customers based on the purchase histories, rental histories, product viewing histories, and/or product ratings of the respective users. Recommendation services are also common for recommending web sites, articles, users, music files, and other types of items.
One problem with current recommendation services is that they typically generate recommendations based on the target user's aggregate activity or profile data, which typically spans a wide range of user interests. For example, in the context of online sales systems, users typically receive recommendations based on their entire shopping histories, even though users commonly shop for many different people and for many different purposes. Recommendations based on such aggregate user activity frequently are not useful.
Some methods currently exist for allowing a user to receive more focused recommendations. For instance, some systems generate recommendations based on the items currently in the user's shopping cart, or based on the items the user has viewed during the current browsing session. In addition, some systems enable users to request recommendations that are specific to a predefined item category (e.g., music). Further, in some systems, the user can specify that a particular event, such as the purchase or viewing of a particular product, should not be taken into consideration for purposes of generating recommendations. Although these features are helpful, a need remains to enable users to more efficiently and flexibly control the process by which recommendations are generated.